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Get Started with Success.ai Today: Partner with us to harness the power of detailed and expansive company data. Whether for enriching your sales processes, conducting in-depth competitor analysis, or enhancing your overall data strategy, Success.ai provides the tools and insights necessary to propel your business to new heights.

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We turn your incomplete contact records into complete customer profiles by filling in the missing pieces. Whether you need emails, phone numbers, company details, or deeper insights, we validate what you have and add what you don't.

Envestnet®| Yodlee®'s Retail Transaction Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

These data contain aggregated survey responses assessing the quality and completeness of metadata for datasets deposited in public repositories and for the same datasets after professional curation.Responses were provided by 10 professional editors representing life, social and physical sciences. Each were randomly assigned four datasets to assess, half (20) of which had been curated according to the standards of Springer Nature's Research Data Support service and half (20) which had not.Curated datasets were shared privately with research participants. The versions that did not receive curation via Springer Nature's Research Data Support are openly accessible.Single-blind testing was employed; the researchers were not made aware which datasets had been curated and which had not, and it was ensured that no participant assessed the same dataset before and after curation. Responses were collected via an online survey. The relevant question and scoring is provided below:Rate the overall quality and completeness of the metadata for the dataset (with regards to finding and accessing and citing the data, not reusing the data)1 = not complete, 5 = very complete

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Our data enrichment solution is built on a powerful identity foundation that delivers comprehensive insights beyond basic contact information:

Unmatched Identity Resolution Our proprietary ASID (Allforce Source ID) system cross-references hundreds of data sources Advanced matching algorithms create accurate, unified contact profiles Seamlessly links professional and personal identities for a complete 360-degree view

Comprehensive Profile Development

Personal Dimensions Complete demographics (name, gender, age range) Lifestyle indicators (marital status, children, homeownership) Financial insights (income range, net worth)

Professional Context Detailed company information (name, domain, revenue, size, industry) Career positioning (job title, seniority, department) Verified business contact details

Contact Verification Phone number validation with type classification (direct, personal, mobile) Address verification with USPS DPV code validation Email validation and deliverability scoring

Digital Footprint Social media profile correlation (LinkedIn) Digital engagement indicators

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Our enrichment process doesn't simply fill data gaps—it reveals valuable connections between professional and personal identities, helping you understand and engage your contacts across both business and consumer contexts.

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Unfortunately, no README file was found for the datano extension, limiting the ability to provide a detailed and comprehensive description. Therefore, the following description is based on the extension name and general assumptions about data annotation tools within the CKAN ecosystem. The datano extension for CKAN, presumably short for "data annotation," likely aims to enhance datasets with annotations, metadata enrichment, and quality control features directly within the CKAN environment. It potentially introduces functionalities for adding textual descriptions, classifications, or other forms of annotation to datasets to improve their discoverability, usability, and overall value. This extension could provide an interface for users to collaboratively annotate data, thereby enriching dataset descriptions and making the data more useful for various purposes. Key Features (Assumed): * Dataset Annotation Interface: Provides a user-friendly interface within CKAN for adding structured or unstructured annotations to datasets and associated resources. This allows for a richer understanding of the data's content, purpose, and usage. * Collaborative Annotation: Supports multiple users collaboratively annotating datasets, fostering knowledge sharing and collective understanding of the data. * Annotation Versioning: Maintains a history of annotations, enabling users to track changes and revert to previous versions if necessary. * Annotation Search: Allows users to search for datasets based on annotations, enabling quick discovery of relevant data based on specific criteria. * Metadata Enrichment: Integrates annotations with existing metadata, enhancing metadata schemas to support more detailed descriptions and contextual information. * Quality Control Features: Includes options to rate, validate, or flag annotations to ensure they are accurate and relevant, improving overall data quality. Use Cases (Assumed): 1. Data Discovery Improvement: Enables users to find specific datasets more easily by searching for datasets based on their annotations and enriched metadata. 2. Data Quality Enhancement: Allows data curators to improve the quality of datasets by adding annotations that clarify the data's meaning, provenance, and limitations. 3. Collaborative Data Projects: Facilitates collaborative data annotation efforts, wherein multiple users contribute to the enrichment of datasets with their knowledge and insights. Technical Integration (Assumed): The datano extension would likely integrate with CKAN's existing plugin framework, adding new UI elements for annotation management and search. It could leverage CKAN's API for programmatic access to annotations and utilize CKAN's security model for managing access permissions. Benefits & Impact (Assumed): By implementing the datano extension, CKAN users can leverage improvements to data discoverability, quality, and collaborative potential. The enhancement can help data curators to refine the understanding and management of data, making it easier to search, understand and promote data driven decision-making.

Environmental enrichment is used to increase social and physical stimulation for animals in captivity which can lead to enhanced cognition. Fundamental to the positive effect enrichment has on the brain is that it provides opportunities for captive animals to recognize and discriminate between different stimuli in the environment. In the wild, being able to discriminate between novel or familiar stimuli has implications for survival, for example finding food, hiding from predators, or even choosing a mate. The novel object recognition (NOR) test is a cognitive task that is used extensively in the rodent literature to assess object recognition and memory, where the amount of time an animal spends exploring a novel vs. familiar object is quantified. Enrichment has been shown to enhance object recognition in rodents. More recently, the use of the NOR test has been applied to another animal model, zebrafish (Danio rerio), however, the effects of enrichment have not yet been explored. In the current study we looked at the effects of enrichment on object recognition in zebrafish using the NOR test. Adult zebrafish were housed in either enriched conditions (gravel substrate, plastic plants, shelter, heater and a filter) or plain conditions (heater and filter only) for 6months before behavioral NOR tests were conducted. Enriched fish showed a preference for a novel object over a familiar one at a distance but did not show a preference during close inspection. Control fish did not show a preference at either distance. Our results suggest that enrichment can enhance zebrafish ability to discriminate between novel and familiar objects, but distance from the object may be an important factor. Future research is needed to determine whether any enhancements in object recognition are a result of an increase in sensory stimulation from being reared with enrichment, or whether it is due to a reduction in stress reactivity.

Envestnet®| Yodlee®'s Bank Transaction Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

Biogenic marine soundscapes provide important navigational cues to dispersing larvae in search of suitable habitat. Yet, widespread habitat loss has degraded marine soundscapes and their functional role in recruitment. Habitat restorations can provide suitable substrate for habitat regeneration, such as reefs constructed to facilitate recruitment and habitat growth by oysters, but typically occur where soundscapes are degraded and recruitment limited. Enhancing marine soundscapes on newly constructed reefs using speaker technology may ensure sufficient recruitment to establish a trajectory of recovery for the desired habitat. Across two of the largest oyster reef restorations in Australia, we deployed speakers at four sites and at three times throughout the recruitment season to test whether soundscape enhancement could boost recruitment and habitat building by oysters. In the presence and absence of soundscape playback, we compared oyster recruitment rates to settlement panels across space and time, and oyster habitat formation on newly constructed boulder reefs. On the settlement panels deployed across the two reef restorations, soundscape playback significantly increased oyster recruitment at 8 of the 10 sites by an average (±1SE) 5.1 ± 1.9 times (5,281 ± 1,384 more larvae per m2), and by as much as 18 times. On boulders atop newly constructed reefs, where the restoration goal is for oysters to form three-dimensional habitat, the surface area covered by oysters after 5 months did not differ between speaker and control treatments. However, soundscape playback appeared to influence the earlier recruitment of oysters, resulting in significantly more large oysters per boulder that formed significantly more three-dimensional habitat building by an average 4.3 ± 1.2 times relative to non-speaker controls. Synthesis and applications. Our results show that using speakers to enhance marine soundscapes boosts the number of oyster recruits, resulting in more larger oysters that form more three-dimensional habitat atop reef restorations. In accelerating the formation of these vertical growth forms, which provide the ecological functions that motivate restoration efforts, the early application of speaker technology on new reef restorations may help steer ecological succession on a trajectory of desired habitat recovery, potentially reducing the substantial cost of ongoing intervention. Methods This study was conducted across two large oyster reef restorations in Gulf St. Vincent, South Australia: Windara Reef (34°30.604″ S, 137°53.949″ E), a 20-hectare reef constructed in 2017–2018, and Glenelg Reef (34°58.314″ S, 138°29.787″ E), a 3-hectare reef constructed in 2020. These two restoration sites, which are approximately 80 km apart on opposite sides of Gulf St. Vincent, are each located ~1 km offshore in 7–10 m of water. To locally enrich marine soundscapes at multiple sites across each restoration, we deployed underwater speakers at two sites across each of Windara Reef and Glenelg Reef. Speaker treatments enriched soundscapes by continuously playing a looped recording of a healthy reef soundscape recorded from a rocky reef habitat located 20 km south of Glenelg Reef (Port Noarlunga Reef). This rocky reef was selected because no flat oyster reefs remain in mainland Australia, and because previous soundscape monitoring throughout Gulf St. Vincent showed this site to be among the most bio-acoustically active. To test the impact of soundscape enrichment on oyster settlement and habitat formation, we assessed oyster recruitment to settlement panels and oyster habitat formation on limestone boulders in the presence and absence of speaker playback. At each site, six plastic crates (40 × 40 × 40 cm) were positioned 2 m apart and 2 m from a speaker (or dummy control) such that they encircled the speaker. These crates provided attachment points for vertical settlement panels and to house limestone boulders. To assess oyster recruitment in space and time, we deployed standardised settlement panels (15 × 15 cm fibreboard) at each site for 1 month to avoid over-saturation by recruits (observed during longer deployments), and repeated these deployments three times throughout the recruitment season. For each time period, divers attached two vertical settlement panels to the outside of each crate, securing them 30 cm above the seafloor using cable ties. After 1 month, settlement panels were removed, and the number of recruited oysters counted from the central 7 × 7 cm area (an area shown to be representative of the entire panel) of the outer surface of the settlement panel under dissection microscope. The number of larvae per tile was calculated per m2 and averaged between the two tiles per crate to provide n = 6 replicate crates per treatment, per site, for each time. At Windara Reef, storms prevented the exchange of speakers to maintain our sound treatments through March, and therefore these data were excluded from the analysis. To assess how soundscape enrichment influences habitat formation on new boulder reefs, we quantified attributes of the habitat formed by oysters on boulders 5 months after the construction of Glenelg Reef. This component was only run at Glenelg Reef for logistical reasons (see manuscript for details). Within a week of reef construction, we placed eight boulders (diameter: 15–30 cm) inside each of the n = 6 crates per site to form independent replicate reefs that reached 30 cm above the seafloor. After 5 months of continual exposure to either speaker or non-speaker control treatments, the top three boulders were removed per crate for analysis in the laboratory. On the exposed upper surface of each boulder, we measured the (1) percentage cover of oyster habitat on each boulder, (2) the number of oysters that were >25 mm in height (the largest size class) as an indication of the earliest recruits to reef boulders and (3) the percentage of early three-dimensional habitat growth (hereafter ‘habitat building’) that was >5 mm above the boulder surface (a height above which no solitary oyster grew, but represented habitat formed by the converging growth of multiple oysters). Boulder surface area and percentage cover was measured in ImageJ from photos taken in the plane of boulder's upper surface. Three-dimensional habitat over >5 mm was manually measured (using a measuring probe) and marked on the boulder surface, after which the percentage cover was measured. Data were averaged across the three boulders per crate (n = 6 per treatment, per site). Full methods available in the published article.

Many metrics are currently used to evaluate the performance of ranking methods in virtual screening (VS), for instance, the area under the receiver operating characteristic curve (ROC), the area under the accumulation curve (AUAC), the average rank of actives, the enrichment factor (EF), and the robust initial enhancement (RIE) proposed by Sheridan et al. In this work, we show that the ROC, the AUAC, and the average rank metrics have the same inappropriate behaviors that make them poor metrics for comparing VS methods whose purpose is to rank actives early in an ordered list (the “early recognition problem”). In doing so, we derive mathematical formulas that relate those metrics together. Moreover, we show that the EF metric is not sensitive to ranking performance before and after the cutoff. Instead, we formally generalize the ROC metric to the early recognition problem which leads us to propose a novel metric called the Boltzmann-enhanced discrimination of receiver operating characteristic that turns out to contain the discrimination power of the RIE metric but incorporates the statistical significance from ROC and its well-behaved boundaries. Finally, two major sources of errors, namely, the statistical error and the “saturation effects”, are examined. This leads to practical recommendations for the number of actives, the number of inactives, and the “early recognition” importance parameter that one should use when comparing ranking methods. Although this work is applied specifically to VS, it is general and can be used to analyze any method that needs to segregate actives toward the front of a rank-ordered list.

Description of the INSPIRE Download Service (predefined Atom): In the Saarland landscape program, large-scale, low-structured landscapes are dealt with separately. These are to be improved in their structuring by means of measures coordinated with agriculture and forestry. However, it should be noted that some areas of structurally poor agricultural landscapes (Saar-Nied-Gau, Moselgau, Wahler Platte) have a high importance as a resting place and partly also breeding ground of endangered bird species of the Offenlan-des (Kiebitz, gold rain plover, Mornell rain plover). In these areas, the structural enrichment measures must be carefully aligned with the concerns of bird protection, so as not to impair them. Structural enhancements in agricultural landscapes are mainly to be created along major economic routes, preferably in the form of high green as connecting axes between settlement areas. The necessary measures must be concretised and presented in the municipal landscape planning. s. landscape program Saarland, chapter 6.5.3 and chapter 10.3.2 (as of 2009) — The link(s) for downloading the records is/are generated dynamically from getFeature Requests to a WFS 1.1.0

The 'opendatagovpy' extension customizes the CKAN platform to align with the specific requirements of datos.gov.py, the Open Data Portal of Paraguay. This extension focuses on user interface enhancements, metadata enrichment, and improved data discovery capabilities to facilitate broader access and usability of open data in Paraguay. By incorporating additional metadata fields and interface elements, it tailors the CKAN instance to effectively serve the needs of its target users. Key Features: * Homepage Banner Carousel: Implements a banner carousel on the main page, providing a dynamic and visually appealing way to highlight key datasets, announcements, or initiatives. * Twitter Widget Integration: Integrates a Twitter widget into the platform, allowing for seamless updates and engagement with the open data community directly from the portal. * Homepage Tag Cloud: Features a tag cloud on the homepage, enhancing discoverability and exploration of datasets by showcasing popular or relevant keywords. * Additional Dataset Metadata Fields: Adds custom fields to the dataset creation and editing form, enabling administrators to record more granular and relevant information about the datasets. * Additional Resource Metadata Fields: Introduces supplementary fields to the resource creation and editing form, allowing for more detailed documentation and characterization of individual data resources. * Indexed Resource Metadata: Indexes the additional metadata fields associated with resources, ensuring that users can effectively search and filter resources based on these custom properties. Benefits & Impact: The 'opendatagovpy' extension enhances the user experience on datos.gov.py. The added metadata fields, both at the dataset and resource levels, contribute to richer descriptions and improved search capabilities. The carrousel banner, integrated Twitter widget, and tag cloud will collectively improve user engagement with the data portal.

ABSTRACT

The complexity of brain circuits is sculpted both by innate genetic programs and environmental stimuli. Since the 1960s scientists have noticed that raising rodents in an enriched environment (EE) is able to improve all aspects of brain plasticity, from learning and memory to visual plasticity in adult and developing animals. Importantly, EE has also been shown to have beneficial effects on a variety of preclinical models of central nervous system diseases: Alzheimer’s and Parkinson’s disease, Rett syndrome, epilepsy etc, prompting intervention protocols in humans. However, the “enrichment derived key signals” through which this special environment performs its broad positive effects on brain health have not been completely elucidated yet. Here, we focused on signals coming from the body periphery and in particular on the gut microbiota. We found that the intestinal microbiota composition of EE mice is significantly different from the one of standard raised (ST) animals. Treatment of EE mice with an antibiotic cocktail completely prevented the EE-driven enhancement of OD plasticity. Strikingly, the fecal microbiota transplant from EE donors to adult ST mice was able to re-activate OD plasticity in the ST recipients. Thus, taken together our data suggest that experience-dependent changes in gut microbiota regulate brain plasticity.

METHODS

In the first dataset (Dataset1, files called zr2423) we report the raw data (.fastq) obtained from the sequencing of the fecal samples from C57BL/6J mice raised in EE or in ST from birth and collected at different time points during their lives.

To analyze the composition of the microbiota of ST and EE mice at different ages, fresh faeces were collected longitudinally in the same subject at postnatal day (P)20 (n=6), P25 (n=6) and P90 (n=6).

In the second dataset (Dataset2, files called zr2747) we report the raw data (.fastq) obtained from the sequencing of the fecal samples from C57BL/6J: adult donor mice living in EE (EE, n=8), adult recipient mice living in ST condition before the fecal transplantation (preFT, n=8) and 4 weeks after the fecal transplantation (postFT, n=8).

For further details about the sample names see the “Explanation Table”.

Bacterial DNA was extracted using a specific kit (QIAamp Powerfecal DNA kit, Qiagen) following the manufacturer's protocol. The 16S rRNA sequencing and analysis was performed by a service offered by Zymo Research (Irvine, CA, USA).

Targeted Library Preparation: The DNA samples were prepared for targeted sequencing with the Quick-16S™ NGS Library Prep Kit (Zymo Research). The primer sets used were Quick-16S™ Primer Set V3-V4 (Zymo Research). The sequencing library was prepared using an innovative library preparation process in which PCR reactions were performed in real-time PCR machines to control cycles and therefore limit PCR chimera formation. The final PCR products were quantified with qPCR fluorescence readings and pooled together based on equal molarity. The final pooled library was cleaned up with the Select-a-Size DNA Clean & Concentrator™, then quantified with TapeStation® (Agilent Technologies, Santa Clara, CA) and Qubit® (Thermo Fisher Scientific, Waltham, WA).

Sequencing: The final library was sequenced on Illumina® MiSeq™ with a v3 reagent kit (600 cycles). The sequencing was performed with >10% PhiX spike-in.

Abell 2199 and Abell 3571 show indications of central abundance enhancements dueto SN Ia ejecta contamination. The mechanisms that create the enhancement arenot clear. Some suggested mechanisms to explain this excess of SN Ia material inthe central regions are: rampressure stripping, SN Ia suppressed winds andnormal stellar mass loss. The discrimination between metal injection mechanismscan be done by determining the distribution of elemental abundance ratios inclusters. We propose to observe A2199 and A3571 for 20 ksec and 30 ksecrespectively (EPIC MOS) to determine the distribution of individual abundancesand abundance ratios. The results will be used in the discrimination of metalenrichment mechanisms, which will provide fundamental constraints on the energetics of the ICM. truncated!, Please see actual data for full text [truncated!, Please see actual data for full text]

1. Disentangling the relative response sensitivity of soil autotrophic (Ra) and heterotrophic respiration (Rh) to nitrogen (N) enrichment is pivotal for evaluating soil carbon (C) storage and stability in the scenario of intensified N deposition. However, the mechanisms underlying differential sensitivities of Ra and Rh and relative contribution of Rh to soil respiration (Rs) with increasing N deposition remains elusive.

2. A manipulative field experiment with multi‐level N addition rates was conducted over three years (2015–2017) in an alpine meadow to explore the relative impact of N enrichment on Ra and Rh and the response of Rh/Rs ratio to the gradient of N addition.

3. Soil respiration components had different sensitivities to N enrichment, with Ra decreasing more than Rh, leading to a higher Rh/Rs ratio as a function of increasing N addition rates. Ra and Rh decreased nonlinearly as N addition rates increased, with a critical load of 8 g N m−2 yr−1 above which N enrichment significantly inhibited them. Ra and Rh were controlled by different abiotic and biotic factors and the regulation of controlling factors on soil respiration components varied over time. N induced reduction in the relative abundance of forb significantly affected Ra and this effect was mainly evident in the second and third year. Nitrogen enrichment significantly changed Rh in the third year and the decreased Rh under high doses of N addition could be attributed to the changes in microbial biomass C, soil substrate quality, and microbial composition.

4. Our study highlights the leading role of Ra in regulating Rs responses to N enrichment and the enhancement of Rh/Rs ratio with increasing N addition. We also emphasize that N induced shifts in plant community composition play a vital role in regulating Ra instead of Rh. The changing drivers of Ra and Rh with time suggests that long‐term experiments with multiple levels of N addition are further needed to test the nonlinear responses and underlying mechanisms of soil respiration components in face to aggravating N deposition.

Zooplankton samples were collected from 14 lakes on the Central and North Coast and the Skeena and Nass systems. The purpose of the sampling was to collect data on zooplankton abundance and species composition from a fertilized lake (Long) and lakes being considered for fertilization under the federal-provincial Salmonid Enhancement Program. All lakes contained anadromous sockeye salmon (Oncorhynchus nerka).

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The Map Service (WMS Group) presents the spatial data from the Saarland landscape program the theme map Forest and Agriculture: The Saarland landscape program deals with large-scale, low-structured landscapes separately. These are to be improved in their structuring by means of measures coordinated with agriculture and forestry. However, it should be noted that some areas of structurally poor agricultural landscapes (Saar-Nied-Gau, Moselgau, Wahler Platte) have a high importance as a resting place and partly also breeding ground of endangered bird species of the openland (Kiebitz, gold rain plover, Mornell rain plover). In these areas, the structural enrichment measures must be carefully aligned with the concerns of bird protection, so as not to impair them. Structural enhancements in agricultural landscapes are mainly to be created along major economic routes, preferably in the form of high green as connecting axes between settlement areas. The necessary measures must be concreteised and presented in municipal landscape planning. see Saarland Landscape Programme, Chapter 6.5.3 and Chapter 10.3.2

Due to widespread nitrogen (N) limitation, reactive N input is expected to stimulate vegetation carbon fixation, thus potentially offsetting decomposition-induced soil carbon losses. However, this projection is largely based on short-term measurements. The crucial question for ecosystem carbon sequestration is whether such responses are sustainable over time. Here, we show that in an N-limited alpine steppe, a decade-long N addition only elicited a transient increase in net ecosystem productivity (NEP); the stimulating effect of N on NEP diminished over time. This phenomenon is likely associated with N-induced phosphorus (P) limitation, as indicated by increases in plant and soil N:P ratios and soil phosphatase activity. The temporal decline in NEP response was reversed with P supplementation, providing experimental evidence of aggravated P limitation under long-term N loadings. Plant metabolomic analysis further revealed that metabolites in the Calvin-Benson cycle and pentose phosphate pathway were particularly sensitive to nutrient changes in the leaves of the dominant species, Leymus secalinus, offering deeper mechanistic insights into the N-induced ecosystem P limitation. Taken together, our findings imply that the N-triggered enhancements in ecosystem carbon sink may be less than previously suggested.